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Proceedings Paper

Semi-supervised segmentation of multispectral remote sensing image based on spectral clustering
Author(s): Xiangrong Zhang; Ting Wang; Licheng Jiao
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Paper Abstract

In this paper, a new multi-spectral remote sensing image segmentation method based on multi-parameter semi-supervised spectral clustering (STS3C) is proposed. Two types of instance-level constraints: must-link and cannot-link are incorporated into spectral cluster to construct semi-supervised spectral clustering in which the self-tuning parameter is applied to avoid the selection of the scaling parameter. Further, when STS3C is applied to multi-spectral remote sensing image segmentation, the uniform sampling technique combined with nearest neighbor rule is used to reduce the computation complexity. Segmentation results show that STS3C outperforms the semi-supervised spectral clustering with fixed parameter and the well-known clustering methods including k-means and FCM in multi-spectral remote sensing image segmentation.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941F (30 October 2009); doi: 10.1117/12.832880
Show Author Affiliations
Xiangrong Zhang, Xidian Univ. (China)
Ting Wang, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing
Faxiong Zhang; Faxiong Zhang, Editor(s)

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